For this assignment, I used Transcriptional profile of human STAT1-/- fibroblasts expressing LY6E or empty control vector data, GSE111958, 11433 genes 6 samples, which about if LY6E will enhance the infectivity of some viruses(Mar KB 2018). In assignment 1, I had already cleaned (remove the low counts) and normalized the data. The link for Assignment 1
gse <- getGEO("GSE111958",GSEMatrix=FALSE)
kable(data.frame(head(Meta(gse))), format = "html")
| contact_address | contact_city | contact_country | contact_department | contact_email | contact_institute |
|---|---|---|---|---|---|
| 5323 Harry Hines Blvd | Dallas | USA | Department of Microbiology | John.Schoggins@UTSouthwestern.edu | UT Southwester Medical Center |
current_gpl <- names(GPLList(gse))[1]
current_gpl_info <- Meta(getGEO(current_gpl))
Platform title : Illumina HiSeq 2500 (Homo sapiens)
Submission data : Mar 14 2013
Last update data : Mar 27 2019
Organims : Homo sapiens
Number of GEO datasets that use this techology : 6188
Number of GEO samples that use this technology : 179516
In assignment 2, I have done differential gene expression analysis and thresholded over-representation analysis. The g:profiler result shows LY6E must play some important role in early step of the virus life cycle and infectivity of some viruses, which is consistent to the results in the paper “LY6E Mediates an Evolutionarily Conserved Enhancement of Virus Infection by Targeting a Late Entry Step.”(Mar KB 2018). The link for Assignment 1
In this assignment, I will take the ranked data that was created in Assignment 2 and perform non-thresholded pathway analysis, then summarize and visualize the results using Cytoscape and the Enrichment map pipeline.
First of all, use the following command to launch Rstudio in Docker.
docker run -e PASSWORD=1234 --rm -p 8787:8787 -v /Users/bfx/Documents/BCB420:/home/rstudio/docker_bcb420 risserlin/bcb420-base-image
In Assignment 2, I have a data frame called qlf_output_hits_withgn which contains the ranked data. Create a new data frame, which only contain the genename and rank, and export to ranked_genelist.rnk
differentially_expressed_genes <- qlf_output_hits_withgn[which(qlf_output_hits_withgn$PValue < 0.05),]
ranked_genelist <- data.frame(differentially_expressed_genes$hgnc_symbol, differentially_expressed_genes$rank)
colnames(ranked_genelist) <- c("GeneName", "rank")
ranked_genelist <- ranked_genelist[nrow(ranked_genelist):1,]
write.table(x=ranked_genelist,
file = file.path("data", "ranked_genelist.rnk"),sep = "\t",
row.names = FALSE,col.names = TRUE, quote = FALSE)
In Homework 3, I downloaded GSEA app (GSEA v4.0.3 Mac App) from https://www.gsea-msigdb.org/gsea/. Now I use it to perform non-thresholded gene set enrichment analysis. Download the baderlab geneset, which contains GO biological process, no IEA and pathways, Human_GOBP_AllPathways_no_GO_iea_February_01_2020_symbol.gmt.
Load data( ranked_genelist.rnk and Human_GOBP_AllPathways_no_GO_iea_February_01_2020_symbol.gmt)
Click Run GSEAPreraned under Tools section
Set Gene sets database be Human_GOBP_AllPathways_no_GO_iea_February_01_2020_symbol.gmt
Set Ranked List be ranked_genelist.rnk
maximum geneset size of 200
minimum geneset size of 15
The top hit for upregulated gene is HALLMARK_INTERFERON_ALPHA_RESPONSE
The top hit for downregulated gene is PID_INTEGRIN1_PATHWAY
In Assignment 2, the top hit for upregulate genes is oxidative phosphorylation (OXPHOS). But for this result, it is more specific to immune system process. The downregulated part is similar. Overall, The two results are both suggest that LY6E can influence the immune system.
Use Cytoscape 3.7.2 to create the Enrichment Map by following the tutorial
The map is very large and no red node, which caused by that there is no hit has FDR q-val < 0.1 in upregulate. So I select show advanced options and set q-value to 0.5, p-value to 0.0001 to reduce the number of nodes and keep enough red node. The enrichment map has 285 nodes and 682 edges
Annotate the network by AutoAnotate and label column with GS_DESCR
remove the clusters which has less than 6 nodes
manually arrange the nodes to avoid the overlap
The legend of the enrichment map
Collapse the network, there are 8 theme:
They fit with the model Since the original paper conclude that LY6E promotes early step of the virus life cycle and enhances infectivity of some viruses and mitochondria is a platform for antiviral innate immunity and antiviral innate immunity requires oxidative phosphorylation (OXPHOS) activity.(Yoshizumi 2017). And also the original paper said the LY6E will not influence the host transcriptome, for these 8 themes, only one is upregulated which also suggest that LY6E may affect the infectivity directly.
For the enrichment results, Most are downregulated. Same with the conclusion of Assignment 2, the mitochondria related theme is upregulated which support the conclusions discussed in the original paper that LY6E promotes early step of the virus life cycle and enhances infectivity of some viruses. Also I found an article “RLR-mediated antiviral innate immunity requires oxidative phosphorylation activity”, published at 2017, said mitochondria is a platform for antiviral innate immunity and antiviral innate immunity requires oxidative phosphorylation (OXPHOS) activity.(Yoshizumi 2017), the downregulate themes seems like also facilitate the infectivity of viruses. Therefore LY6E must play some important role in early step of the virus life cycle and infectivity of some viruses.
Load the Human_TranscriptionFactors_MSigdb_April_01_2020_symbol.gmt from web, set test to Mann-Whitney (one side Greater)
the original paper concluded the LY6E does not regulate IFN-mediated gene expression which means it will not influence the host transcriptome. The result shows that LY6E do play some important role in early step of the virus life cycle and infectivity of some viruses, since the ATP synthesis and immune system process are upregulated. Therefore LY6E may enhance viral infectivity directly instead of influencing the cellular transcriptome.
Mar KB, Boys IN, Rinkenberger NR. 2018. “LY6E Mediates an Evolutionarily Conserved Enhancement of Virus Infection by Targeting a Late Entry Step.” Nat Commun 9 (11): 3603.
Yoshizumi, Imamura, T. 2017. “RLR-Mediated Antiviral Innate Immunity Requires Oxidative Phosphorylation Activity.” Sci Rep 7: 5379.